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Vivek Natarajan
AI Researcher, Google
Vivek Natarajan is a Research Scientist at Google with a strong focus on large language models (LLMs) and biomedicine, leading groundbreaking AI research.
Vivek is renowned for developing Med-PaLM, Med-PaLM 2, Med-PaLM M, and AMIE, cutting-edge AI systems with exceptional performance in medical applications.
His work has been recognized in prestigious publications like Nature, Nature Medicine, and conferences such as CVPR, ICCV, and NeurIPS.
Vivek's contributions extend to regulated medical devices like Mammo Reader and DermAssist, revolutionizing breast cancer detection and skin condition classification.
Prior to Google, Vivek excelled at Facebook AI Research, accumulating patents, awards, and deploying AI models to serve millions of users.
With a strong academic background, Vivek pursued Masters in Computer Science at The University of Texas at Austin and B.Tech in Electronics and Communication Engineering at NIT Tiruchirappalli.
His experience spans various roles including Faculty at Harvard T.H. Chan School of Public Health, AI Researcher at Meta, Venture Fellow at Healthspan Capital, and Teaching Assistant at UT Austin.
Vivek's research interests include multimodal assistant systems, diagnostic dialogue, and AI applications in healthcare, showcasing a versatile skillset at the forefront of technological innovation.
Highlights
Scientific discovery and clinical medicine are often treated as distinct phases. But for patients with rare, complex, and undiagnosed diseases, this separation is a luxury they cannot afford. The timeline from understanding a genetic mechanism to accessing subspecialist care is often too long and too fragmented.
Two new @GoogleDeepMind @GoogleResearch collaborations with @StanfordMed , published in Advanced Science and @NatureMedicine respectively last week, demonstrate how AI can bridge this gap.
- Accelerating discovery (the science) In Advanced Science, we present one of the first wet-lab validated examples of AI-assisted genetic discovery.
Our AI identified a novel genetic factor for hearing loss (Crym) in mice, which Dr Gary Peltz and team validated using CRISPR knock-in experiments to restore the wild-type gene and rescue the phenotype.
We applied this agentic AI scaffold to human patients with complex, undiagnosed conditions in a retrospective manner. The system analyzed genomic data for rare diseases, such as IRAK4 deficiency and ODC1 mutations, successfully identifying causative variants that matched expert clinical assessments.
- Scaling expertise (the medicine) Discovery is only the first step; patients then need access to specialized care.
As we note in our Nature Medicine paper, hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death, yet ~60% of patients remain undiagnosed due to a lack of specialist centers .
In our RCT (one of the first of its kind) using our research AI system AMIE, we showed AI could help bridge this gap. General cardiologists using AMIE reported the system helped their assessments in 57.0% of cases, missed no clinically significant findings in 93.5% of cases and reduced assessment time in 50.5% of cases. This suggests the AI can act as a helpful co-pilot and help generalists bridge the gap to specialists.
Worth noting that these studies used models like Med-PaLM 2, Gemini 2.0 Flash, and Gemini 2.5 Pro with simple agentic scaffolds.
The potential for Gemini 3 and AI co-scientist to accelerate both the biology of discovery and the delivery of care is profound and we will share more soon.
Its a true privilege to collaborate with @euanashley , Jack W O'Sullivan, Dr Gary Peltz and their teams at Stanford Medicine.
With incredible team mates at Google including @taotu831 @apalepu13 , @alan_karthi , @Mysiak and many more.
Advanced Science paper - https://t.co/lkj0soEGuj
Nature Medicine paper - https://t.co/G38tExCakN
AI co-scientist blog - https://t.co/Br5JegvcF6
AMIE blog - https://t.co/iLErUQRsSW

During Thanksgiving break last year, our AI co-scientist team @GoogleDeepMind @GoogleResearch - @Mysiak @alan_karthi met Prof @jrpenades @CostaT_Lab of @ImperialCollege.
They were nearing a breakthrough on how bacteria share resistance genes and proposed a test for our AI co-scientist: pose their exact research question to the system and see what it found.
Today in @CellCellPress, we share the results.
Paper link - https://t.co/h2x5C1bU2p

